Numerical Model Study of In Vivo Magnetic Nanoparticle Tumor Heating.
Numerical Model Study of In Vivo Magnetic Nanoparticle Tumor Heating.
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体内磁性纳米颗粒肿瘤加热的数值模型研究。
DOI：
10.1109/tbme.2017.2666738
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Hoopes,PJack
中科院分区：
文献类型：
--
作者：
Pearce,JohnA;Petryk,AliciaA;Hoopes,PJack
纳米氧化铁目前正在作为肿瘤热疗的加热剂进行研究。有效加热的主要决定因素包括生物分布和在实际可达到的磁场强度下实现充分加热所需的最低氧化铁负荷。这些相互关联的标准最终决定了这种治疗肿瘤的方法的实用性。此外，根据我们的经验，目前使用的热疗治疗评估标准--43摄氏度下的累积当量分钟，目的将数值模型与实验测量相结合，以研究细胞死亡预测所描述的相对加热效率。方法应用有限元数值模型来增加对一系列精心校准的小鼠乳腺腺癌实验的理解。结果数值模型结果表明，在162 kHZ的磁场强度下，达到实验观察到的32kA/m(RMS)的温度所需的最小肿瘤负荷为每cm3肿瘤组织约1.3至1.8毫克铁。结论：在数值模型中包含多个细胞死亡过程的并行操作提供了有价值的前景成功治疗的可能性。显着性我们表明并相信这些评估方法比仅基于热史比较的单一评估品质因数更准确，例如CEM方法。
Iron oxide nanoparticles are currently under investigation as heating agents for hyperthermic treatment of tumors. Major determinants of effective heating include the biodistribution and minimum iron oxide loading required to achieve adequate heating at practically achievable magnetic field strengths. These inter-related criteria ultimately determine the practicality of this approach to tumor treatment. Further, in our experience the currently used treatment assessment criterion for hyperthermia treatment-cumulative equivalent minutes at 43 °C, CEM43- provides an inadequate description of the expected treatment effectiveness.ObjectivesCouple numerical models to experimental measurements to study the relative heating effectiveness described by cell death predictions.MethodsFEM numerical models were applied to increase the understanding of a carefully calibrated series of experiments in mouse mammary adenocarcinoma.ResultsThe numerical model results indicate that minimum tumor loadings between approximately 1.3 to 1.8 mg of Fe per cm3of tumor tissue are required to achieve the experimentally observed temperatures in magnetic field strengths of 32 kA/m (rms) at 162 kHz.ConclusionWe show that including multiple cell death processes operating in parallel within the numerical models provides valuable perspective on the likelihood of successful treatment.SignificanceWe show and believe that these assessment methods are more accurate than a single assessment figure of merit based only on the comparison of thermal histories, such as the CEM method.